Full body scanners—laser scanners/array of depth cameras (such as the Kinect® by Microsoft Corporation) can be used to capture three dimensional (3D) points on the body's surface, which can be used to represent the body shape as in point form (e.g. cloud of points), surface form (mesh of triangles), or other models. Such models can be analyzed to extract the required measurements. For example, the waist location can be computed from body proportions and the cross section at waist height can be used to extract a list of points or a contour representing the circumference at that point. The perimeter is than computed from said 3D list of points/contour.
The cost and complexity of building a full body scanner are prohibitive for mass deployment in stores and/or consumer in-home usage. Therefore, several prior art techniques describe how to extract specific body measurements from a single two dimensional (2D) image (usually a front view) and optionally a few other additional views of controlled pose (e.g. a side view). Considering for example the waist circumference, it is clear that one or two views cannot provided enough information to compute the exact circumference without further assumptions or approximation—for example an elliptical cross section of the body at the waist area. Clearly such approximations fail to provide exact measures when real subjects are involved.
Other prior art methods are more flexible with regard to environment, or user pose, but rely on parameterized and statistical shape models in order to solve for the shape of a specific user. For example, US 2010/0111370 discloses a system and method of estimating the body shape of an individual from input data such as images or range maps. The disclosed method captures the statistical variability across a human population with a smaller number of parameters (e.g., fewer than 100). To represent a wide variety of human shapes with a low-dimensional model, statistical learning is used to model the variability of body shape across a population (or sub-population). It is clear however, that quite often, relying on statistical models may result in significant errors for any single user, as compared with the requirements for garment fittings or for monitoring patients undergoing treatment. In such applications, accuracy requirements are 98% or better.
Furthermore, to be commercially viable, the measurement process will be performed by the inexperienced user at his/her apartment and without additional human guidance/observation. The process must be short and exhibit a high rate of success—in the first attempt.
It is an object of the present invention to provide a system which is capable of automatically guiding the user through a sequence of body poses and motions that include at least partial rotation.
It is another object of the present invention to provide a system which is capable of ensuring that the user correctly follows guiding instructions provided by the system.
It is yet another embodiment of the present invention to provide a system which is capable of providing instructions and ensuring steps that relates to the choice of measurement site/scene, camera positions, measurement garment color and tightness, and other environmental parameters that may aid to improve the quality of the measurements.
Other objects and advantages of the invention will become apparent as the description proceeds.